军用网格环境下基于优先权的Min-Min任务调度算法

军用网格环境下的资源调度与一般网格环境下的资源调度相比较，一个明显的特点就是必须考虑一些特别任务的优先级。在给出网格独立任务调度模型基础上，提出了一种基于优先权的Min—Min资源调度算法，该算法首先调度优先级高的任务，其余任务则采用Min—Min算法调度。经过分析，该算法的时间复杂度是O（n^2m），与Min—Min相比，该算法的Makespan可能略大，但可以满足军用网格环境下特殊任务优先执行的需求。     

计算网格环境下以QoS为指导的启发式任务调度

实现用户的服务质量QoS是网格的一个重要特征．网格环境下以服务质量为指导的任务调度是一个非常复杂且具有挑战性的问题，特别是在用户的任务具有多种QoS需求的情况下。利用效用函数对用户的多种QoS进行建模．提出了一个同时兼顾任务完成时间和用户服务质量的目标函数。在此基础上提出了一种计算网格环境下针对一组具有QoS需求的相互独立的计算任务的启发式调度算法。模拟实验结果显示，该算法能较好的满足不同用户的需求并提升系统资源的利用率。     

面向多功能雷达的模型参数化脉冲级调度方法

多功能相控阵雷达的任务复杂性和实时性需求不断提高,传统的基于一定驻留周期的任务调度方法不能满足实时切换要求。针对此问题,提出了一种基于脉冲周期的任务切换和实时调度的方法,两级调度器与执行器一体化设计减少任务调度时间开销,综合考虑优先级调度算法和抢占式调度策略确保了任务有效执行,面向任务模型的参数化设计使得任务调度能灵活适应各类作战任务需求。仿真结果表明,此方法能较好地控制高优先级任务丢失率, 获得更高的任务执行有效性。     

虚拟化移动核心网任务调度算法研究

针对虚拟化移动核心网的任务调度问题进行研究。Min-Min调度算法是一个简单、快速、有效的算法,但是它很难满足移动用户对服务质量的要求。提出一种基于虚拟机响应时间的改进算法。在虚拟机与元任务映射过程中,首先按照Min-Min算法进行匹配,然后计算每个与元任务匹配的虚拟机的响应时间,将任务划分为满足响应时限和不满足两部分,对满足响应时限的任务进行调度,为不满足调度响应时限的任务重新分配一个空闲的虚拟机中执行时间最短的虚拟机,尽最大可能避免任务执行时间超出响应时限,导致任务调度失败,进而影响用户满意度。在虚拟机和元任务一定的情况下,分别仿真这两种算法的调度过程,得到任务完成时间、任务成功率和负载情况图,对仿真实验结果进行对比分析。     

基于遗传算法的网格任务调度研究

网格调度是一个典型的NP问题,研究表明遗传算法是解决复杂环境下资源调度的有效方法之一。针对异构环境下的网格任务调度,提出了一种改进的遗传算法,并改进了遗传算法的收敛特性,通过分析实验结果证明该算法优于传统的调度算法。     

QIACO:一种多QoS约束网格任务调度算法

网格环境下的任务调度问题属于NP难解,难以得到精确的最优解,适合使用蚁群算法等智能优化算法对最优解进行逼近;同时,服务质量(QoS)也是衡量网格性能的一个重要指标,网格任务调度应该满足用户的QoS需求.为解决具有QoS保证的网格任务调度问题.本文以带有Qos约束的任务为研究对象,结合改进的蚁群算法,提出了一种基于蚁群算...     

基于VR虚拟漫游系统的任务树交互设计应用研究

为了提高VR虚拟漫游系统的运行效率,从而改善用户体验,提出一种基于任务树的人机交互任务调度算法。首先对VR漫游系统的工作流程进行介绍,分析了实时人机交互的重要性。然后将人机交互任务分解为具有优先级的树状层次结构,并在确保按照优先级调度的条件下,通过对比执行时间来消除CPU调度的盲目性,从而提高运行速度。仿真实验结果表明,相比先来先服务(FCFS)调度算法和短作业优先(SJF)调度算法,提出的人机交互任务调度算法在平均响应时间和平均等待时间方面表现出更好的性能。     

启发式动态任务调度与航路规划方法

针对任务、资源、航路的调度与规划需要,建立了一种分层递阶的任务规划系统结构,上层主要解决任务优先级和冲突消解问题;下层主要解决满足各种要求的航路规划与实时重规划问题。提出了启发式动态任务调度与航路实时规划算法,可以有效提高对各种突发事件的态势自适应和冲突消解能力以及对时敏目标的打击能力,进而提高无人机在动态不确定环境下的自主性。     

云计算环境下任务调度算法的研究

在云计算环境中存在庞大的任务数,为了能更加高效地完成任务请求,如何进行有效地任务调度是云计算环境下实现按需分配资源的关键。针对调度问题提出了一种基于蚁群优化的任务调度算法,该算法能适应云计算环境下的动态特性,且集成了蚁群算法在处理NP-Hard问题时的优点。该算法旨在减少任务调度完成时间。通过在CloudSim平台进行仿真实验,实验结果表明,改进后的算法能减少任务平均完成时间、并能在云计算环境下有效提高调度效率。     

移动网格中依赖性任务调度

本文研究移动网格环境下的依赖性任务调度问题,提出预调度加重调度的动态调度策略.预调度利用改进的HEFT算法将所有任务映射到当前可用资源,目标是总完成时间最短,同时考虑了资源的能量约束条件.任务运行过程中监测资源的各种异常行为,当满足重调度触发条件时进行重调度,从而实现对动态环境的自适应性.通过实例分析表明了调度策略的有效性.     

一种基于延迟的队列调度实现

PQBEDF算法是一种将优先级和时延相结合的动态优先级调度算法,具有快速高效的特点。对PQBEDF算法进行了研究,对其实现过程进行了改进,并给出了具体实现方法,同时对队列长度和优先级之间的关系作了分析。改进后的算法简化了操作,避免了PQBEDF算法中优先级可能相同的不合理现象,提高了算法的鲁棒性。另外,改进后的算法在公平性上也有所提高,不仅满足高优先级业务对带宽和时延的要求,对低优先级业务也有一定的保障,为各业务提供既有一定保证又有所区别的服务,具有一定的公平性和合理性。     

边缘计算多约束可信协同任务迁移策略

为保障边缘计算的服务质量,提出一种在多约束条件下边缘计算可信协同任务迁移策略。该策略基于任务需求,由边缘计算协同服务盟主节点组织调度协同服务盟员,基于用户任务迁移的K维权重指标,确定协同盟员调度优先级,以盟员负载均衡性为适应函数,通过贪心算法执行盟员任务分配与调度,基于路由捎带选择备用节点,通过迁移优先级评估,实现协同服务异常时的调度和迁移,由此提高边缘计算任务迁移的服务质量,保障任务迁移的可靠性。仿真实验表明,该机制能有效完成协同任务分发与迁移调度,提高边缘计算协同效率,保障网络服务质量。     

An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems

Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling.

     

Network queue scheduling algorithm based on self-similar traffic level grading prediction

Self-similarity characteristic of network traffic will lead to the continuous burstness of data in the network.In order to effectively reduce the queue delay and packet loss rate caused by network traffic burst,improve the transmission capacity of different priority services,and guarantee the service quality requirements,a queue scheduling algorithm P-DWRR based on the self-similarity of network traffic was proposed.A dynamic weight allocation method and a service quantum update method based on the self-similar traffic level grading prediction results were designed,and the service order of the queue according was determined to the service priority and queue waiting time,so as to reduce the queuing delay and packet loss rate.The simulation results show that the P-DWRR algorithm can reduce the queueing delay,delay jitter and packet loss rate on the basis of satisfying the different service priority requirements of the network,and its performance is better than that of DWRR and VDWRR.     

Task scheduling algorithm for system-wide information management based on multiple QoS constraints

An ant colony optimization task scheduling algorithm based on multiple quality of service constraint (QoS-ACO) for SWIM was proposed.Focusing on the multiple quality of service (QoS) requirements for task requests completed in system-wide information management (SWIM),considering the task execution time,security and reliability factors,a new evaluate user satisfaction utility function and system task scheduling model were constructed.Using the QoS total utility evaluation function of SWIM service scheduling to update the pheromone of the ant colony algorithm.The simulation results show that under the same conditions,the QoS-ACO algorithm is better than the traditional Min-Min algorithm and particle swarm optimization (PSO) algorithm in terms of task completion time,security,reliability and quality of service total utility evaluation value,and it can ensure that the user's task scheduling quality of service requirements are met,and can better complete the scheduling tasks of the SWIM.     

ForCES网络中的资源调度算法和经济模型(英文)

Resource scheduling algorithm for ForCES(Forwarding and Control Element Separation) networks need to meet the flexibility,programmability and scalability of node resources.DBC(Deadline Budget Constrain) algorithm relies on users select cost or time priority,then scheduling to meet the requirements of users.However,this priority strategy of users is relatively simple,and cannot adapt to dynamic change of resources,it is inevitable to reduce the QoS.In order to improve QoS,we refer to the economic model and resource scheduling model of cloud computing,use SAL(Service Level Agreement) as pricing strategy,on the basis of DBC algorithm,propose an DABP(Deadline And Budget Priority based on DBC) algorithm for ForCES networks,DABP combines both budget and time priority to scheduling.In simulation and test,we compare the task finish time and cost of DABP algorithm with DP(Deadline Priority) algorithm and BP(Budget Priority) algorithm,the analysis results show that DABP algorithm make the task complete with less cost within deadline,benifical to load balancing of ForCES networks.     

基于QoS的网格工作流的选择调度算法

开放网格服务架构（OGSA）和计算经济模型的提出,使得动态的、不同QoS的服务支持下的资源调度成为一个复杂且具有挑战性的问题。文中提出了基于QoS的网格工作流的选择调度算法,该算法采用动态资源选择策略适应网格计算环境下的动态性和自治性。在满足用户对服务质量要求的基础上,平衡了工作流中服务实例的花费、处理时间和可用性之间的关系。试验证明该调度算法符合计算网格的复杂环境,能够更好地满足不同用户的实际需要。     

Efficient scheduling discipline for Hierarchical Diff‐EDF

Packet networks are currently enabling the integration of traffic with a wide range of characteristics that extend from video traffic with stringent quality of service (QoS) requirements to the best‐effort traffic requiring no guarantees. QoS guarantees can be provided in conventional packet networks by the use of proper packet‐scheduling algorithms. As a computer revolution, many scheduling algorithms have been proposed to provide different schemes of QoS guarantees, with Earliest Deadline First (EDF) as the most popular one. With EDF scheduling, all flows receive the same miss rate regardless of their traffic characteristics and deadlines. This makes the standard EDF algorithm unsuitable for situations in which the different flows have different miss rate requirements since in order to meet all miss rate requirements it is necessary to limit admissions so as to satisfy the flow with the most stringent miss rate requirements. In this paper, we propose a new priority assignment scheduling algorithm, Hierarchal Diff‐EDF (Differentiate Earliest Deadline First), which can meet the real‐time needs of these applications while continuing to provide best‐effort service to non‐real time traffic. The Hierarchal Diff‐EDF features a feedback control mechanism that detects overload conditions and modifies packet priority assignments accordingly. Copyright © 2007 John Wiley & Sons, Ltd.     

数字阵列雷达在线交错调度算法

传统相控阵雷达中的调度算法难以充分发挥数字阵列雷达的多功能优势.针对这一问题,结合数字阵列雷达的任务结构,提出一种在线交错调度算法.通过将交错调度分析分解为时间资源约束分析和能量资源约束分析,算法能够对所有满足约束的任务进行交错调度：利用任务中的等待期来交错执行其它任务的发射期或接收期,并且不同任务的接收期可实现相互交叠.仿真结果表明,由于雷达任务中等待期和接收期得到充分利用,相比于三种传统的调度算法,所提算法的调度成功率、实现价值率和时间利用率均得到有效提升.